Unknotting the interactive effects of learning processes on cultural evolutionary dynamics

Scanlon, Lauren; Lobb, Andrew; Tehrani, Jamshid J.; Kendal, Jeremy

doi:10.1017/ehs.2019.17

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…Problems in need of more research are marked as . of attention in recent years and have also been applied (with varying success) to cultural phenomena (Carrignon et al, 2019;Crema et al, 2014Crema et al, , 2016Kandler et al, 2017;Kandler & Powell, 2015;Rubio-Campillo, 2016;Scanlon et al, 2019). One of the major stumbling blocks to these inference techniques is the curse of dimensionality and the consequential use of summary statistics, which reduce complex, multidimensional observations to a low-dimensional space.…”

Section: Discussionmentioning

confidence: 99%

“…Because the likelihood (the probability density for a given observation) is often intractable in complex simulation models, solutions are sought that bypass the computation of the likelihood. These so-called likelihood-free inference techniques – or, more generally, simulation-based inference techniques (Cranmer et al, 2019 ) – have been the focus of attention in recent years and have also been applied (with varying success) to cultural phenomena (Carrignon et al, 2019 ; Crema et al, 2014 , 2016 ; Kandler et al, 2017 ; Kandler & Powell, 2015 ; Rubio-Campillo, 2016 ; Scanlon et al, 2019 ). One of the major stumbling blocks to these inference techniques is the curse of dimensionality and the consequential use of summary statistics, which reduce complex, multidimensional observations to a low-dimensional space.…”

Section: Discussionmentioning

confidence: 99%

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Classifying evolutionary forces in language change using neural networks

et al. 2020

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Classifying evolutionary forces in language change using neural networks

et al. 2020

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“…Aspects of this process can be studied experimentally. Behavioural studies (reviewed in [9]) show that cumulative innovation under controlled laboratory conditions allows experimental micro-societies to discover increasingly functional solutions to problem-solving and optimization tasks in both physical [10][11][12][13][14] and virtual [15][16][17][18] settings (e.g. paper aeroplane [10] or virtual arrowhead [16] design tasks).…”

Section: Introductionmentioning

confidence: 99%

Human biases limit cumulative innovation

Thompson

Griffiths

2021

Proc. R. Soc. B.

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Is technological advancement constrained by biases in human cognition? People in all societies build on discoveries inherited from previous generations, leading to cumulative innovation. However, biases in human learning and memory may influence the process of knowledge transmission, potentially limiting this process. Here, we show that cumulative innovation in a continuous optimization problem is systematically constrained by human biases. In a large ( n = 1250) behavioural study using a transmission chain design, participants searched for virtual technologies in one of four environments after inheriting a solution from previous generations. Participants converged on worse solutions in environments misaligned with their biases. These results substantiate a mathematical model of cumulative innovation in Bayesian agents, highlighting formal relationships between cultural evolution and distributed stochastic optimization. Our findings provide experimental evidence that human biases can limit the advancement of knowledge in a controlled laboratory setting, reinforcing concerns about bias in creative, scientific and educational contexts.

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“…Aspects of this process can be studied experimentally. Behavioral studies (reviewed in [9]) show that cumulative innovation under controlled laboratory conditions allows experimental micro-societies to discover increasingly functional solutions to problem-solving and optimization tasks in both physical [10,11,12,13,14] and virtual [15,16,17,18] settings (e.g. paper aeroplane [10] or virtual arrowhead [16] design tasks).…”

mentioning

confidence: 99%

Human Biases Limit Cumulative Innovation

Thompson¹,

Griffiths²

2021

Preprint

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Is technological advancement constrained by biases in human cognition? People in all societies build on discoveries inherited from previous generations, leading to cumulative innovation. However, biases in human learning and memory may influence the process of knowledge transmission, potentially limiting this process. Here we show that cumulative innovation in a continuous optimization problem is systematically constrained by human biases. In a large (n = 1,250) behavioral study using a transmission chain design, participants searched for virtual technologies in one of four environments after inheriting a solution from previous generations. Participants converged on worse solutions in environments misaligned with their biases. These results substantiate a mathematical model of cumulative innovation in Bayesian agents, highlighting formal relationships between cultural evolution and distributed stochastic optimization. Our findings provide experimental evidence that human biases can limit the advancement of knowledge in a controlled laboratory setting, reinforcing concerns about bias in creative, scientific, and educational contexts.

show abstract

Unknotting the interactive effects of learning processes on cultural evolutionary dynamics

Abstract: Abstract

Cited by 5 publications

References 21 publications

Classifying evolutionary forces in language change using neural networks

Classifying evolutionary forces in language change using neural networks

Human biases limit cumulative innovation

Human Biases Limit Cumulative Innovation

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